Soham Gade
Visualisations
Data Visualisations are created using Python libraries such as Matplotlib, Seaborn & Plotly. Interactive visualisations are created using Tableau. Data Stories are present on my Tableau Public Profile
World Happiness Report
Plotly 1
A world map that shows the happiness level scores of countries from the year 2007 to 2021.
You can navigate to each country to see their values according to the year of your choice.
You can also zoom in or zoom out, the map to see countries of a certain region.
There are various factors contributing to this score which will be seen shortly.
- Through the figure:
- It is observed that most of the countries/states present in North American & ANZ and the Western European regions have maintained a high levels of happiness throughtout many years.
- Similarly, it can be observed that most of the countries present in Sub-Saharan African, Asian & Southeast Asian regions have happiness levels fluctuating between low to medium. Indicating a lower score happiness score throughout many years.
Matplotlib & Seaborn Vizz
The mean happiness level score was 5.0
Any country above or equal to the mean score were termed as happiest or progressing. On the other hand, any country whose mean score was less than 5 were termed as unhappy or struggling.
- Two plots were made:
- Showing the top 10 happiest countries throughout multiple years.
- Showing the top 10 lowest happiness scored countries throughout multiple years.
Scores calculated and used, were the mean scores of all the years for that country.
Plot 1
Plot 2
Conclusion
Denmark & Finland are the first and second highest mean happiness level scored countries, respectively, throughtout all the years.
Whereas, South Sudan & Afghanistan are the lowest and second lowest mean happiness level scored countries, respectively, throughout many years.
What are the factors contributing to this score and why do the country's score differ so much?
Let's find out!
Correlated Factors of Happiness
It seems that, GDP per capita score, Healthy life expectancy & Social Support of a country, are the main factors contributing to the overall happiness level!
Surprisingly, GDP score and Healthy life expectancy are most closely related! Let's see how!
Healthy Life vs GDP
You can hover over each scatter plot to the see the country's name, its Region and its exact GDP per capita score!
It is observed that as the GDP per capita of a country increases, the healthy life expectancy of that country increases as well.
As seen in our first geo graph, most of the countries in The Sub-Saharan African region has a low GDP score and thus a low healthy life expectancy.
Conclusion
We can say that:
- The most important factors leading to an increase in the happiness levels of any country are the GDP per capita of that entity, the social support it has and the healthy life expectancy of that country.
- Other factors that contribute in an overall good happiness score are Freedom & Positive Affectivity
Although, all of these criterias are interrelated.
Thus most of the countries in the affected regions, such as Sub-Saharan Afria, South Asia & some countries in the Latin American Region must focus on the GDP score and how to improve health of its citizens.
This will in return increase the positive affectivity in its citizens, thus increasing social support and contributing to an overall higher happiness level.
While on the other hand, it is observed, countries having a progressive increase in its GDP score, such a Singapore in the Southeast Asian region have an increasing healthy life expectany throughout multiple years.
These also include most of the countries present in the Western European Region.